cms-model 0.1.0

Coordinated Minima Search: An Efficient Approach for Optimizing Linear and Non-Linear Regression Models.

CMS: Co-ordinated Minima Search

Overview

CMS is a Python package that provides tools for compiling and managing custom models for linear and non-linear regression analysis. It includes functions to compile linear and non-linear regression models, and to predict.

Features

• Takes a pandas dataframe at the input
• Can compile the weight and bias variables for both linear and non-linear regression models.
• Can predict output values following a given regression model.

Installation

1. Clone the repository:

   git clone https://github.com/RHassan1609/cms.git
   

2. Install the package:

   pip install .
   

Usage

To use the CMS package in your project, import the module and use the available functions.

from cms import compile_custom_model, compile_best_model, compile_all_models, predict

# Example: Using the compile_custom_model function

import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_custom_model(data=data,powers=[1,2,1],feature_columns=[0,1,2],label_column=4,print_loss=True,model_name='cms_model',print_model=True)

# Example: Using the compile_best_model function

import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_best_model(data=data,max_power=2,feature_columns=[0,1,2],label_column=4,print_loss=True,print_best_model=True,best_model_name='best_cms_model',save_first_model=True,first_model_name='first_cms_model')

# Example: Using the compile_all_models function

import pandas as pd
data=pd.read_csv(r'D:\CMS\training_data.csv')
compile_all_models(data=data,powers=[1,2,1],feature_columns=[0,1,2],label_column=4,print_loss=True,model_name='cms_model',print_model=True)

# Example: Using the predict function
test_data=pd.read_csv(r'D:\CMS\test_data.csv')
predict(data=test_data,load_model='best_cms_model',print_output=True,save_output_file=True,saved_file_name='predicted cms file')